A Neural Machine Interface Architecture for Real-Time Artificial Lower Limb Control
Jason Kane, Qing Yang, Robert Hernandez, Willard Simoneau and Matthew Seaton
Department of Electrical, Computer, and Biomedical Engineering,
University of Rhode Island, Kingston, RI, 02881, USA
This paper presents a novel architecture of a lower limb neural machine interface (NMI) for determination of user intent. Our new design and implementation paves the way for future bionic legs that require high speed real-time deterministic response, high accuracy, easy portability, and low power consumption. A working FPGA-based prototype has been built, and experiments have shown that it achieves average performance gains of around 8x that of the equivalent software algorithm running on an Intel Core i7 2670QM, or 24x that of an Intel Atom Z530 with no perceivable loss in accuracy. Furthermore, our fully pipelined and parallel non-linear support vector machine-based FPGA implementation led to a 6.4x speedup over an equivalent GPU-based design. In this paper, we also characterize our achieved timing margin to show that our design is capable of supporting real-time wireless communications. With additional refinement, such a wireless personal area network (PAN) system will provide improved flexibility on an individual basis for electromyography (EMG) sensor placement.
Keywords: Field Programmable Gate Arrays (FPGA), Support Vector Machines (SVM), Neural Machine Interface (NMI), Artificial leg control, Parallel architectures.
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